Visual statistical learning in the newborn infant

Predictive coding and attention in developmental cognitive neuroscience and perspectives for neurodevelopmental disorders

Sensory prediction and repetition suppression are closely related cognitive mechanisms that allow the brain to form predictions about the environment, and guide perception in synergy with attention. Predictive coding is a theory of the fundamental role of predictive mechanisms in brain functions. Authors have proposed a central role of predictive impairments in autism and possibly other neurodevelopmental disorders. However, little is known about predictive mechanisms in typical development, and how they co-develop with attention. Here we review experimental support for predictive coding and its links with attention in healthy adults' brains, the first experimental works performed in typically developing children and infants, and theoretical accounts of neurodevelopmental disorders using a predictive coding framework. We propose future directions for predictive coding research in development. Finally, we describe the first predictive coding experiments in neonates and provide research perspectives for using this framework in searching for early markers of atypical neurodevelopment.

The size and shape of sound: The role of articulation and acoustics in iconicity and crossmodal correspondencesa)

Onomatopoeias like hiss and peep are iconic because their forms resemble their meanings. Iconicity can also involve forms and meanings in different modalities, such as when people match the nonce words bouba and kiki to round and angular objects, and mil and mal to small and large ones, also known as "sound symbolism." This paper focuses on what specific analogies motivate such correspondences in spoken language: do people associate shapes and size with how phonemes sound (auditory), or how they are produced (articulatory)? Based on a synthesis of empirical evidence probing the cognitive mechanisms underlying different types of sound symbolism, this paper argues that analogies based on acoustics alone are often sufficient, rendering extant articulatory explanations for many iconic phenomena superfluous. This paper further suggests that different types of crossmodal iconicity in spoken language can fruitfully be understood as an extension of onomatopoeia: when speakers iconically depict such perceptual characteristics as size and shape, they mimic the acoustics that are correlated with these characteristics in the natural world.

Statistical learning beyond words in human neonates

Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infant's toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech -speaker identity and phonemes- to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension. In two experiments, we exposed neonates to artificial speech streams constructed by concatenating syllables while recording EEG. The sequence had a statistical structure based either on the phonetic content, while the voices varied randomly (Experiment 1) or on voices with random phonetic content (Experiment 2). After familiarisation, neonates heard isolated duplets adhering, or not, to the structure they were familiarised with. In both experiments, we observed neural entrainment at the frequency of the regularity and distinct Event-Related Potentials (ERP) to correct and incorrect duplets, highlighting the universality of statistical learning mechanisms and suggesting it operates on virtually any dimension the input is factorised. However, only linguistic duplets elicited a specific ERP component, potentially an N400 precursor, suggesting a lexical stage triggered by phonetic regularities already at birth. These results show that, from birth, multiple input regularities can be processed in parallel and feed different higher-order networks.

Neural tracking of auditory statistical regularities in adults with and without dyslexia

Listeners implicitly use statistical regularities to segment continuous sound input into meaningful units, eg transitional probabilities between syllables to segment a speech stream into separate words. Implicit learning of such statistical regularities in a novel stimulus stream is reflected in a synchronization of neural responses to the sequential stimulus structure. The present study aimed to test the hypothesis that neural tracking of the statistical stimulus structure is reduced in individuals with dyslexia who have weaker reading and spelling skills, and possibly also weaker statistical learning abilities in general, compared to healthy controls. To this end, adults with and without dyslexia were presented with continuous streams of (non-speech) tones, which were arranged into triplets, such that transitional probabilities between single tones were higher within triplets and lower between triplets. We found that the so-called Triplet Learning Index (ie the ratio of neural phase coherence at the triplet rate relative to the tone rate) was lower in adults with dyslexia compared to the control group. Moreover, a higher Triplet Learning Index was associated with better spelling skills. These results suggest that individuals with dyslexia have a rather broad deficit in processing structure in sound instead of a merely phonological deficit.

Successful sensitization of 2.5-year-olds to other-race faces through bimodal training

The present study investigated the potential for sensitizing 2.5-year-old Caucasian infants to other-race faces (Asian faces). In the domain of face perception, infants become less sensitive to facial distinctions of other-race faces through perceptual narrowing at the end of the first year of life. Nevertheless, infants around 12 months can regain their sensitivity to other-race faces. For instance, exposing them to a specific statistical distribution and employing the mechanisms of statistical learning is one way to enhance their discriminatory abilities towards other-race faces. Following this idea, we investigated if even older infants around 2.5 years can be sensitized to other-race faces. We trained the infants with a bimodal distribution of a morphed continuum of Asian female faces with faces closer to the endpoints presented most frequently. We assessed infants' discrimination of Asian faces by measuring their looking times after the training phase. The 2.5-year-olds showed a difference in looking times after the training, indicating that the exposure to a bimodal frequency distribution led to a successful discrimination between Asian faces. These findings demonstrate that 2.5-year-olds can be sensitized to other-race faces by exposing them to a bimodal distribution of such faces, underlining the plasticity of face perception in childhood.

Emotional stimuli boost incidental learning through predictive processing

Extracting regularities and probabilities from the environment is a fundamental and critical ability in an ever-changing surrounding. Previous findings showed that people are highly efficient in learning these regularities and that emotional stimuli are better learned than neutral ones. Yet, the generality and the underlying mechanism of this benefit are poorly understood. Here, participants viewed a stream of images with negative and neutral valence. Unbeknownst, the items recurred in regularity as triplets. Then, to assess learning, a surprised familiarity test was conducted. The results of Experiment 1, using two sets of stimuli, found better statistical learning for negative triplets than for neutral triplets. Experiment 2 revealed similar benefits even when only a single negative item was in the triplet at the second or third position, suggesting the advantage is not cumulative. We speculated that the predictability of the negative items is driving the effect. Consequently, Experiment 3 confirmed that the memory for neutral items preceding negative items was better than for neutral items preceding neutral items. Together, these findings provide novel insights into the mechanism of how the learning of incidental temporal associations is influenced by negative stimuli and the role of predictability in the negative valence benefit.

Digital Twin Studies for Reverse Engineering the Origins of Visual Intelligence

What are the core learning algorithms in brains? Nativists propose that intelligence emerges from innate domain-specific knowledge systems, whereas empiricists propose that intelligence emerges from domain-general systems that learn domain-specific knowledge from experience. We address this debate by reviewing digital twin studies designed to reverse engineer the learning algorithms in newborn brains. In digital twin studies, newborn animals and artificial agents are raised in the same environments and tested with the same tasks, permitting direct comparison of their learning abilities. Supporting empiricism, digital twin studies show that domain-general algorithms learn animal-like object perception when trained on the first-person visual experiences of newborn animals. Supporting nativism, digital twin studies show that domain-general algorithms produce innate domain-specific knowledge when trained on prenatal experiences (retinal waves). We argue that learning across humans, animals, and machines can be explained by a universal principle, which we call space-time fitting. Space-time fitting explains both empiricist and nativist phenomena, providing a unified framework for understanding the origins of intelligence.

The Cognitive Architecture of Infant Attachment

Meta-analytic evidence indicates that the quality of the attachment relationship that infants establish with their primary caregiver has enduring significance for socioemotional and cognitive outcomes. However, the mechanisms by which early attachment experiences contribute to subsequent development remain underspecified. According to attachment theory, early attachment experiences become embodied in the form of cognitive-affective representations, referred to as internal working models (IWMs), that guide future behavior. Little is known, however, about the cognitive architecture of IWMs in infancy. In this article, we discuss significant advances made in the field of infant cognitive development and propose that leveraging insights from this research has the potential to fundamentally shape our understanding of the cognitive architecture of attachment representations in infancy. We also propose that the integration of attachment research into cognitive research can shed light on the role of early experiences, individual differences, and stability and change in infant cognition, as well as open new routes of investigation in cognitive studies, which will further our understanding of human knowledge. We provide recommendations for future research throughout the article and conclude by using our collaborative research as an example.

Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills

Visual statistical Learning (SL) allows infants to extract the statistical relationships embedded in a sequence of elements. SL plays a crucial role in language and communication competencies and has been found to be impacted in Autism Spectrum Disorder (ASD). This study aims to investigate visual SL in infants at higher likelihood of developing ASD (HL-ASD) and its predictive value on autistic-related traits at 24-36 months. At 6 months of age, SL was tested using a visual habituation task in HL-ASD and neurotypical (NT) infants. All infants were habituated to a visual sequence of shapes containing statistically predictable patterns. In the test phase, infants viewed the statistically structured, familiar sequence in alternation with a novel sequence that did not contain any statistical information. HL-ASD infants were then evaluated at 24-36 months to investigate the associations between visual SL and ASD-related traits. Our results showed that NT infants were able to learn the statistical structure embedded in the visual sequences, while HL-ASD infants showed different learning patterns. A regression analysis revealed that SL ability in 6-month-old HL-ASD infants was related to social communication and interaction abilities at 24-36 months of age. These findings indicate that early differences in learning visual statistical patterns might contribute to later social communication skills.

Beat processing in newborn infants cannot be explained by statistical learning based on transition probabilities

Newborn infants have been shown to extract temporal regularities from sound sequences, both in the form of learning regular sequential properties, and extracting periodicity in the input, commonly referred to as a regular pulse or the 'beat'. However, these two types of regularities are often indistinguishable in isochronous sequences, as both statistical learning and beat perception can be elicited by the regular alternation of accented and unaccented sounds. Here, we manipulated the isochrony of sound sequences in order to disentangle statistical learning from beat perception in sleeping newborn infants in an EEG experiment, as previously done in adults and macaque monkeys. We used a binary accented sequence that induces a beat when presented with isochronous timing, but not when presented with randomly jittered timing. We compared mismatch responses to infrequent deviants falling on either accented or unaccented (i.e., odd and even) positions. Results showed a clear difference between metrical positions in the isochronous sequence, but not in the equivalent jittered sequence. This suggests that beat processing is present in newborns. Despite previous evidence for statistical learning in newborns the effects of this ability were not detected in the jittered condition. These results show that statistical learning by itself does not fully explain beat processing in newborn infants.

The effect of hippocampal subfield damage on rapid temporal integration through statistical learning and associative inference

INTRODUCTION

The hippocampus (HPC) supports integration of information across time, often indexed by associative inference (AI) and statistical learning (SL) tasks. In AI, an indirect association between stimuli that never appeared together is inferred, whereas SL involves learning item relationships by extracting regularities across experiences. A recent model of hippocampal function (Schapiro et al., 2017) proposes that the HPC can support temporal integration in both paradigms through its two distinct pathways.

METHODS

We tested this models' predictions in four patients with varying degrees of bilateral HPC damage and matched healthy controls, with two patients with complementary damage to either the monosynaptic or trisynaptic pathway. During AI, participants studied overlapping paired associates (AB, BC) and their memory was tested for premise pairs (AB) and for inferred pairs (AC). During SL, participants passively viewed a continuous picture sequence that contained an underlying structure of triplets that later had to be recognized.

RESULTS

Binomial distributions were used to calculate above chance performance at the individual level. For AI, patients with focal HPC damage were impaired at inference but could correctly infer pairs above chance once premise pair acquisition was equated to controls; however, the patient with HPC and cortical damage showed severe impairment at recalling premise and inferred pairs, regardless of accounting for premise pair performance. For SL, none of the patients performed above chance, but notably neither did most controls.

CONCLUSIONS

Associative inference of indirect relationships can be intact with HPC damage to either hippocampal pathways or the HPC more broadly, provided premise pairs can first be formed. Inference may remain intact through residual HPC tissue supporting premise pair acquisition, and/or through extra-hippocampal structures supporting inference at retrieval. Clear conclusions about hippocampal contributions to SL are precluded by low performance in controls, which we caution is not dissimilar to previous amnesic studies using the same task. This complicates interpretations of studies claiming necessity of hippocampal contributions to SL and warrants the use of a common and reliable task before conclusions can be drawn.

Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech

Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)-a degraded form of speech not immediately perceptible as speech-as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism.

Realistic and broad-scope learning simulations: first results and challenges

There is a current 'theory crisis' in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.

How do human newborns come to understand the multimodal environment?

For a long time, newborns were considered as human beings devoid of perceptual abilities who had to learn with effort everything about their physical and social environment. Extensive empirical evidence gathered in the last decades has systematically invalidated this notion. Despite the relatively immature state of their sensory modalities, newborns have perceptions that are acquired, and are triggered by, their contact with the environment. More recently, the study of the fetal origins of the sensory modes has revealed that in utero all the senses prepare to operate, except for the vision mode, which is only functional starting from the first minutes after birth. This discrepancy between the maturation of the different senses leads to the question of how human newborns come to understand our multimodal and complex environment. More precisely, how the visual mode interacts with the tactile and auditory modes from birth. After having defined the tools that newborns use to interact with other sensory modalities, we review studies across different fields of research such as the intermodal transfer between touch and vision, auditory-visual speech perception, and the existence of links between the dimensions of space, time, and number. Overall, evidence from these studies supports the idea that human newborns are spontaneously driven, and cognitively equipped, to link information collected by the different sensory modes in order to create a representation of a stable world.

Eight-Month-Old Infants Meta-Learn by Downweighting Irrelevant Evidence

Infants learn to navigate the complexity of the physical and social world at an outstanding pace, but how they accomplish this learning is still largely unknown. Recent advances in human and artificial intelligence research propose that a key feature to achieving quick and efficient learning is meta-learning, the ability to make use of prior experiences to learn how to learn better in the future. Here we show that 8-month-old infants successfully engage in meta-learning within very short timespans after being exposed to a new learning environment. We developed a Bayesian model that captures how infants attribute informativity to incoming events, and how this process is optimized by the meta-parameters of their hierarchical models over the task structure. We fitted the model with infants' gaze behavior during a learning task. Our results reveal how infants actively use past experiences to generate new inductive biases that allow future learning to proceed faster.

Quantity discrimination in newly hatched zebrafish suggests hardwired numerical abilities

An intriguing hypothesis to explain the ubiquity of numerical abilities is that all vertebrates are born with hardwired neuronal networks for processing numbers. To date, only studies on human foetuses have clearly supported this hypothesis. Zebrafish hatch 48-72 h after fertilisation with an embryonic nervous system, providing a unique opportunity for investigating this hypothesis. Here, we demonstrated that zebrafish larvae exposed to vertical bars at birth acquired an attraction for bar stimuli and we developed a numerical discrimination task based on this preference. When tested with a series of discriminations of increasing difficulty (1vs.4, 1vs.3, 1vs.2, and 2vs.4 bars), zebrafish larvae reliably selected the greater numerosity. The preference was significant when stimuli were matched for surface area, luminance, density, and convex hull, thereby suggesting a true capacity to process numerical information. Converging results from two phylogenetically distant species suggests that numerical abilities might be a hallmark feature of vertebrates' brains.

The Grossberg Code: Universal Neural Network Signatures of Perceptual Experience

Two universal functional principles of Grossberg’s Adaptive Resonance Theory decipher the brain code of all biological learning and adaptive intelligence. Low-level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom-up activation and under the control of top-down matching rules that integrate high-level, long-term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are re-visited in this concept paper on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg’s pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics.

The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability

In the present study we examined whether infants' visual prediction abilities were related to different types of motion experiences. We tested 30 6-month-old infants on a visual-spatial prediction task in which they had to visually anticipate the locational reappearance of temporarily occluded moving objects. We assigned infants to one of three experience groups: active locomotion training, passive motion experience, and a no-training control group. We tested the infants' visual prediction abilities before and after these trainings. We found improved infant predictions at a post-training test only for passively trained infants (p = .015, d = -1.033; Bonferroni corrected). Thus, we conclude that infants' visual-spatial predictions of temporally occluded moving objects was facilitated by mere movement experience, even if passive. Visual information gathered during even passive movement seemed sufficient for visual prediction.

Novel phonotactic learning by children and infants: Generalizing syllable-position but not co-occurrence regularities

Restrictions in the sequencing of sounds (phonotactic constraints) can be represented at the level of sound co-occurrences (e.g., in baF.Pev, F and P co-occur) and at the level of the syllable (e.g., F is syllable-coda/end, P is syllable-onset/start). Can children (5-year-olds) and infants (11-month-olds) represent constraints as sound co-occurrences and/or relative to syllable positions? Participants listened to artificial languages displaying both word-medial consonant restrictions in co-occurrence pairs (e.g., FP or DZ but not FZ) and in the position of consonants within syllables (e.g., P/Z onsets and D/F codas) in words like baF.Pev and tiD.Zek. Children responded similarly to novel words with the same (e.g., FP) versus different (e.g., FZ) co-occurrence pairs, but they were more misled (i.e., responded "heard it before") by novel words with consonants in the same (e.g., onset-P) versus different (e.g., coda-P) syllable positions (Experiment 1). With the same training stimuli, infants had similar orientation times for novel words with the same versus different co-occurrence pairs, but they had longer orientation times for novel words with consonants in the same versus different syllable positions (Experiment 2). Thus, across different methods and ages, syllable-position information was more readily available for generalization than consonant co-occurrence information. The results suggest that when multiple regularities are present simultaneously, some phonotactic constraints (e.g., consonants in particular syllable positions) may be spontaneously represented and generalized by children and infants, whereas others (e.g., consonant co-occurrences) might not be available. The results contribute toward understanding how children and infants represent sound sequences.

Modality, presentation, domain and training effects in statistical learning

While several studies suggest that the nature and properties of the input have significant effects on statistical learning, they have rarely been investigated systematically. In order to understand how input characteristics and their interactions impact statistical learning, we explored the effects of modality (auditory vs. visual), presentation type (serial vs. simultaneous), domain (linguistic vs. non-linguistic), and training type (random, starting small, starting big) on artificial grammar learning in young adults (N = 360). With serial presentation of stimuli, learning was more effective in the auditory than in the visual modality. However, with simultaneous presentation of visual and serial presentation of auditory stimuli, the modality effect was not present. We found a significant domain effect as well: a linguistic advantage over nonlinguistic material, which was driven by the domain effect in the auditory modality. Overall, the auditory linguistic condition had an advantage over other modality-domain types. Training types did not have any overall effect on learning; starting big enhanced performance only in the case of serial visual presentation. These results show that input characteristics such as modality, presentation type, domain and training type influence statistical learning, and suggest that their effects are also dependent on the specific stimuli and structure to be learned.

Somatosensory prediction in the premature neonate brain

Sensory prediction (SP) is at the core of early cognitive development. Impaired SP may be a key to understanding the emergence of neurodevelopmental disorders, however there is little data on how and when this skill emerges. We set out to provide evidence of SP in the brain of premature neonates in the fundamental sensory modality: touch. Using Diffuse Correlation Spectroscopy, we measured blood flow changes in the somatosensory cortex of premature neonates presented with a vibrotactile stimulation-omission sequence. When ISI was fixed, participants presented a decrease in blood flow during stimulus omissions, starting when a stimulus should begin: the expectation of a certain stimulus onset induced deactivation of the somatosensory cortex. When ISI was jittered, we observed an increase in blood flow during omissions: the expectation of a likely but not certain stimulus onset induced activation of the somatosensory cortex. Our results reveal SP in the brain as early as four weeks before term, based on the temporal structure of a unimodal somatosensory stimulation, and show that SP produces opposite regulation of activity in the somatosensory cortex depending on how liable is stimulus onset. Future studies will investigate the predictive value of somatosensory prediction on neurodevelopment in this vulnerable population.

The relation between rhythm processing and cognitive abilities during child development: The role of prediction

Rhythm and meter are central elements of music. From the very beginning, children are responsive to rhythms and acquire increasingly complex rhythmic skills over the course of development. Previous research has shown that the processing of musical rhythm is not only related to children’s music-specific responses but also to their cognitive abilities outside the domain of music. However, despite a lot of research on that topic, the connections and underlying mechanisms involved in such relation are still unclear in some respects. In this article, we aim at analyzing the relation between rhythmic and cognitive-motor abilities during childhood and at providing a new hypothesis about this relation. We consider whether predictive processing may be involved in the relation between rhythmic and various cognitive abilities and hypothesize that prediction as a cross-domain process is a central mechanism building a bridge between rhythm processing and cognitive-motor abilities. Further empirical studies focusing on rhythm processing and cognitive-motor abilities are needed to precisely investigate the links between rhythmic, predictive, and cognitive processes.

An Implicit-Explicit Framework for Intervention Methods in Developmental Language Disorder

PURPOSE

The growing interest in framing intervention approaches as either implicit or explicit calls for a discussion of what makes intervention approaches engage each of these learning systems, with the goal of achieving a shared framework. This tutorial presents evidence for the interaction between implicit and explicit learning systems, and it highlights the intervention characteristics that promote implicit or explicit learning as well as outcome measures that tap into implicit or explicit knowledge. This framework is then applied to eight common intervention approaches and notable combinations of approaches to unpack their differential engagement of implicit and explicit learning.

CONCLUSIONS

Many intervention characteristics (e.g., instructions, elicitation techniques, feedback) can be manipulated to move an intervention along the implicit-explicit continuum. Given the bias for using explicit learning strategies that develops throughout childhood and into adulthood, clinicians should be aware that most interventions (even those that promote implicit learning) will engage the explicit learning system. However, increased awareness of the implicit and explicit learning systems and their cognitive demands will allow clinicians to choose the most appropriate intervention for the target behavior.

Differential activation of a frontoparietal network explains population-level differences in statistical learning from speech

People of all ages display the ability to detect and learn from patterns in seemingly random stimuli. Referred to as statistical learning (SL), this process is particularly critical when learning a spoken language, helping in the identification of discrete words within a spoken phrase. Here, by considering individual differences in speech auditory–motor synchronization, we demonstrate that recruitment of a specific neural network supports behavioral differences in SL from speech. While independent component analysis (ICA) of fMRI data revealed that a network of auditory and superior pre/motor regions is universally activated in the process of learning, a frontoparietal network is additionally and selectively engaged by only some individuals (high auditory–motor synchronizers). Importantly, activation of this frontoparietal network is related to a boost in learning performance, and interference with this network via articulatory suppression (AS; i.e., producing irrelevant speech during learning) normalizes performance across the entire sample. Our work provides novel insights on SL from speech and reconciles previous contrasting findings. These findings also highlight a more general need to factor in fundamental individual differences for a precise characterization of cognitive phenomena.

In the context of speech, statistical learning is thought to be an important mechanism for language acquisition. This study shows that language statistical learning is boosted by the recruitment of a fronto-parietal brain network related to auditory-motor synchronization and its interplay with a mandatory auditory-motor learning system.

Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion

During the early postnatal years, most infants rapidly learn to understand two naturally evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain-general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.

Statistical Learning in Vision

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Role of Attention, Language Ability, and Language Experience in Children's Artificial Grammar Learning

PURPOSE

The current study examined the role of attention and language ability in nonverbal rule induction performance in a demographically diverse sample of school-age children.

METHOD

The participants included 43 English-speaking monolingual and 65 Spanish-English bilingual children between the ages of 5 and 9 years. Core Language Index standard scores from the Clinical Evaluation of Language Fundamentals-Fourth Edition indexed children's language skills. Rule induction was measured via a visual artificial grammar learning task. Two equally complex finite-state artificial grammars were used. Children learned one grammar in a low attention condition (where children were exposed to symbol sequences with no distractors) and another grammar in a high attention condition (where distractor symbols were presented around the perimeter of the target symbol sequences).

RESULTS

Overall, performance in the high attention condition was significantly worse than performance in the low attention condition. Children with robust language skills performed significantly better in the high attention condition than children with weaker language skills. Despite group differences in socioeconomic status, English language skills, and nonverbal intelligence, monolingual and bilingual children performed similarly to each other in both conditions.

CONCLUSION

The results suggest that the ability to extract rules from visual input is attenuated by the presence of competing visual information and that language ability, but not bilingualism, may influence rule induction.

Sleeping neonates track transitional probabilities in speech but only retain the first syllable of words

Extracting statistical regularities from the environment is a primary learning mechanism that might support language acquisition. While it has been shown that infants are sensitive to transition probabilities between syllables in speech, it is still not known what information they encode. Here we used electrophysiology to study how full-term neonates process an artificial language constructed by randomly concatenating four pseudo-words and what information they retain after a few minutes of exposure. Neural entrainment served as a marker of the regularities the brain was tracking during learning. Then in a post-learning phase, evoked-related potentials (ERP) to different triplets explored which information was retained. After two minutes of familiarization with the artificial language, neural entrainment at the word rate emerged, demonstrating rapid learning of the regularities. ERPs in the test phase significantly differed between triplets starting or not with the correct first syllables, but no difference was associated with subsequent violations in transition probabilities. Thus, our results revealed a two-step learning process: neonates segmented the stream based on its statistical regularities, but memory encoding targeted during the word recognition phase entangled the ordinal position of the syllables but was still incomplete at that age.

Sequential learning of emotional faces is statistical at 12 months of age

Infants are capable of extracting statistical regularities from continuous streams of elements, which helps them structuring their surrounding environment. The current study examines 12-month-olds' capacity to extract statistical information from a sequence of emotional faces. Using a familiarization procedure, infants were presented with videos of two actresses expressing the same facial emotion, and subsequently turning toward or away from each other. Videos displayed different emotions (i.e., anger, happiness, fear, sadness, surprise, amusement, disgust, and exasperation) and were organized sequentially, so that the transitional probabilities between videos were highly predictable in some cases, and less predictable in others. At test, infants discriminated highly predictable from low predictable transitional probabilities, suggesting that they extracted statistical regularities from the sequence of emotional faces. However, when examining the looking toward and the looking away conditions separately, infants showed evidence of statistical learning in the looking toward condition only. Together, these findings suggest that 12-month-old infants rely on statistical learning to segment a continuous sequence of emotional faces, although this ability can be modulated by the nature of the stimuli. The contribution of statistical learning to structure infants' social environment is discussed.

Knowledge of Statistics or Statistical Learning? Readers Prioritize the Statistics of their Native Language Over the Learning of Local Regularities

A large body of evidence suggests that people spontaneously and implicitly learn about regularities present in the visual input. Although theorized as critical for reading, this ability has been demonstrated mostly with pseudo-fonts or highly atypical artificial words. We tested whether local statistical regularities are extracted from materials that more closely resemble one’s native language. In two experiments, Italian speakers saw a set of letter strings modelled on the Italian lexicon and guessed which of these strings were words in a fictitious language and which were foils. Unknown to participants, words could be distinguished from foils based on their average bigram frequency. Surprisingly, in both experiments, we found no evidence that participants relied on this regularity. Instead, lexical decisions were guided by minimal bigram frequency, a cue rooted in participants’ native language. We discuss the implications of these findings for accounts of statistical learning and visual word processing.

Visual Implicit Learning Abilities in Infants at Familial Risk for Language and Learning Impairments

The ability of infants to track transitional probabilities (Statistical Learning—SL) and to extract and generalize high-order rules (Rule Learning—RL) from sequences of items have been proposed as being pivotal for the acquisition of language and reading skills. Although there is ample evidence of specific associations between SL and RL abilities and, respectively, vocabulary and grammar skills, research exploring SL and RL as early markers of language and learning (dis)abilities is still scarce. Here we investigated the efficiency of visual SL and RL skills in typically developing (TD) seven-month-old infants and in seven-month-old infants at high risk (HR) for language learning impairment. Infants were tested in two visual-habituation tasks aimed to measure their ability to extract transitional probabilities (SL task) or high-order, repetition-based rules (RL task) from sequences of visual shapes. Post-habituation looking time preferences revealed that both TD and HR infants succeeded in learning the statistical structure (SL task), while only TD infants, but not HR infants, were able to learn and generalize the high-order rule (RL task). These findings suggest that SL and RL may contribute differently to the emergence of language learning impairment and support the hypothesis that a mechanism linked to the extraction of grammar structures may contribute to the disorder.

The Role of Stimulus-Specific Perceptual Fluency in Statistical Learning

Humans have the ability to learn surprisingly complicated statistical information in a variety of modalities and situations, often based on relatively little input. These statistical learning (SL) skills appear to underlie many kinds of learning, but despite their ubiquity, we still do not fully understand precisely what SL is and what individual differences on SL tasks reflect. Here, we present experimental work suggesting that at least some individual differences arise from stimulus‐specific variation in perceptual fluency: the ability to rapidly or efficiently code and remember the stimuli that SL occurs over. Experiment 1 demonstrates that participants show improved SL when the stimuli are simple and familiar; Experiment 2 shows that this improvement is not evident for simple but unfamiliar stimuli; and Experiment 3 shows that for the same stimuli (Chinese characters), SL is higher for people who are familiar with them (Chinese speakers) than those who are not (English speakers matched on age and education level). Overall, our findings indicate that performance on a standard SL task varies substantially within the same (visual) modality as a function of whether the stimuli involved are familiar or not, independent of stimulus complexity. Moreover, test–retest correlations of performance in an SL task using stimuli of the same level of familiarity (but distinct items) are stronger than correlations across the same task with stimuli of different levels of familiarity. Finally, we demonstrate that SL performance is predicted by an independent measure of stimulus‐specific perceptual fluency that contains no SL component at all. Our results suggest that a key component of SL performance may be related to stimulus‐specific processing and familiarity.

Effects of healthy aging and left hemisphere stroke on statistical language learning

Spoken sentences are continuous streams of sound, without reliable acoustic cues to word boundaries. We have previously proposed that language learners identify words via an implicit statistical learning mechanism that computes transitional probabilities between syllables. Neuroimaging studies in healthy young adults associate this learning with left inferior frontal gyrus, left arcuate fasciculus, and bilateral striatum. Here, we test the effects of healthy aging and left hemisphere (LH) injury on statistical learning. Following 10-minute exposure to an artificial language, participants rated familiarity of Words, Part-words (sequences spanning word boundaries), and Non-words (unfamiliar sequences). Young controls (N=14) showed robust learning, rating Words>Part-words>Non-words. Older controls (N=28) showed this pattern to a weaker degree. Stroke survivors (N=24) as a group showed no learning. A lesion comparison examining individual differences revealed that "non-learners" are more likely to have anterior lesions. Together, these findings demonstrate that word segmentation is sensitive to healthy aging and LH injury.

Consciousness Beyond Neural Fields: Expanding the Possibilities of What Has Not Yet Happened

In the field theories in physics, any particular region of the presumed space-time continuum and all interactions between elementary objects therein can be objectively measured and/or accounted for mathematically. Since this does not apply to any of the field theories, or any other neural theory, of consciousness, their explanatory power is limited. As discussed in detail herein, the matter is complicated further by the facts than any scientifically operational definition of consciousness is inevitably partial, and that the phenomenon has no spatial dimensionality. Under the light of insights from research on meditation and expanded consciousness, chronic pain syndrome, healthy aging, and eudaimonic well-being, we may conceive consciousness as a source of potential energy that has no clearly defined spatial dimensionality, but can produce significant changes in others and in the world, observable in terms of changes in time. It is argued that consciousness may have evolved to enable the human species to generate such changes in order to cope with unprecedented and/or unpredictable adversity. Such coping could, ultimately, include the conscious planning of our own extinction when survival on the planet is no longer an acceptable option.

EXPRESS: Are animates special? Exploring the effects of selective attention and animacy on visual statistical learning

Our visual system is built to extract regularities in how objects within our visual environment appear in relation to each other across time and space ('visual statistical learning'). Existing research indicates that visual statistical learning is modulated by selective attention. Our attentional system prioritises information that enables behaviour; for example, animates are prioritised over inanimates (the 'animacy advantage'). The present study examined the effects of selective attention and animacy on visual statistical learning in young adults (N = 284). We tested visual statistical learning of attended and unattended information across four animacy conditions: (i) living things that can self-initiate movement (animals); (ii) living things that cannot self-initiate movement (fruits and vegetables); (iii) non-living things that can generate movement (vehicles); and (iv) non-living things that cannot generate movement (tools and kitchen utensils). We implemented a four-point confidence-rating scale as an assessment of participants' awareness of the regularities in the visual statistical learning task. There were four key findings. First, selective attention plays a critical role by modulating visual statistical learning. Second, animacy does not play a special role in visual statistical learning. Third, visual statistical learning of attended information cannot be exclusively accounted for by unconscious knowledge. Fourth, performance on the visual statistical learning task is associated with the proportion of stimuli that were named or labelled. Our findings support the notion that visual statistical learning is a powerful mechanism by which our visual system resolves an abundance of sensory input over time.

Variability in Quantity and Quality of Early Linguistic Experience in Children With Cochlear Implants: Evidence from Analysis of Natural Auditory Environments

OBJECTIVES

Understanding how quantity and quality of language input vary across children with cochlear implants (CIs) is important for explaining sources of large individual differences in language outcomes of this at-risk pediatric population. Studies have mostly focused either on intervention-related, device-related, and/or patient-related factors, or relied on data from parental reports and laboratory-based speech corpus to unravel factors explaining individual differences in language outcomes among children with CIs. However, little is known about the extent to which children with CIs differ in quantity and quality of language input they experience in their natural linguistic environments. To address this knowledge gap, the present study analyzed the quantity and quality of language input to early-implanted children (age of implantation <23 mo) during the first year after implantation.

DESIGN

Day-long Language ENvironment Analysis (LENA) recordings, derived from home environments of 14 early-implanted children, were analyzed to estimate numbers of words per day, type-token ratio (TTR), and mean length of utterance in morphemes (MLUm) in adults' speech. Properties of language input were analyzed across these three dimensions to examine how input in home environments varied across children with CIs in quantity, defined as number of words, and quality, defined as whether speech was child-directed or overheard.

RESULTS

Our per-day estimates demonstrated that children with CIs were highly variable in the number of total words (mean ± SD = 25,134 ± 9,267 words) and high-quality child-directed words (mean ± SD = 10,817 ± 7,187 words) they experienced in a day in their home environments during the first year after implantation. The results also showed that the patterns of variability across children in quantity and quality of language input changes depending on whether the speech was child-directed or overheard. Children also experienced highly different environments in terms of lexical diversity (as measured by TTR) and morphosyntactic complexity (as measured by MLUm) of language input. The results demonstrated that children with CIs varied substantially in the quantity and quality of language input experienced in their home environments. More importantly, individual children experienced highly variable amounts of high-quality, child-directed speech, which may drive variability in language outcomes across children with CIs.

CONCLUSIONS

Analyzing early language input in natural, linguistic environments of children with CIs showed that the quantity and quality of early linguistic input vary substantially across individual children with CIs. This substantial individual variability suggests that the quantity and quality of early linguistic input are potential sources of individual differences in outcomes of children with CIs and warrant further investigation to determine the effects of this variability on outcomes.

Visual statistical learning in infancy: Discrimination of fine-grained regularities depends on early test trials

Infants' ability to detect statistical regularities between visual objects has been demonstrated in previous studies (e.g., Kirkham et al., Cognition, 83, 2002, B35). The extent to which infants extract and learn the actual values of the transitional probabilities (TPs) between these objects nevertheless remains an open question. In three experiments providing identical learning conditions but contrasting different types of sequences at test, we examined 8-month-old infants' ability to discriminate between familiar sequences involving high or low values of TPs, and new sequences that involved null TPs. Results showed that infants discriminate between these three types of sequences, supporting the existence of a statistical learning mechanism by which infants extract fine-grained statistical information from a stream of visual stimuli. Interestingly, the expression of this statistical knowledge varied between experiments and specifically depended on the nature of the first two test trials. We argue that the predictability of this early test arrangement-namely whether the first two test items were either predictable or unexpected based on the habituation phase-determined infants' looking behaviors.

Is there such a thing as a 'good statistical learner'?

A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.

The memory benefits of two naps per day during infancy: A pilot investigation

In infancy, sleep occurs in multiple nap and overnight bouts that change developmentally in quantity and distribution. Though studies suggest that infant memory benefits from a single nap, no work has assessed the relative benefits of different naps (morning vs. afternoon), nor how multiple naps support memory across the day. We investigated the memory benefit of a morning nap, relative to morning wake, and the effect of these intervals on afternoon nap function in 9-month-olds (n = 15). Infants participated in two within-subjects conditions (separated by 1-2 weeks). In the Nap-Nap condition, infants took their morning and afternoon naps; in the Wake-Nap condition, infants were kept awake during morning naptime, but napped unrestricted in the afternoon. Before each nap/wake interval, infants completed an imitation memory task, with memory assessed again shortly after the nap/wake interval. In the Nap-Nap condition, infants showed memory retention across morning and afternoon naps. In contrast, infants tended to forget items learned across morning wake in the Wake-Nap condition. Moreover, morning wake was associated with a significant decline in post-nap retention of items learned in the afternoon. Furthermore, relations between nap slow-wave activity (SWA) and memory varied across naps, with SWA either not predicting (morning naps) or positively predicting (afternoon naps) memory change in the Nap-Nap condition, but negatively predicting afternoon memory change in the Wake-Nap condition. We conclude that two naps per day (rather than one) aids memory at 9 months, and that skipping the morning nap may moderate relations between afternoon nap physiology and memory.

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected only in the structured condition, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in areas around the left pre-central gyrus as well as right temporo-frontal areas significantly predicted behavioral performance. Sensor level results confirmed this relationship between neural entrainment and subsequent explicit knowledge. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two aspects are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on factors such as sufficient exposure time and attention.

Language statistical learning responds to reinforcement learning principles rooted in the striatum

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

Statistical learning is the ability to extract regularities from the environment; in the domain of language, this ability is fundamental in the learning of words and structural rules. This study uses a combination of computational modelling and functional MRI to reveal a fundamental link between online language statistical learning and reinforcement learning at the algorithmic and implementational levels.

Child-directed speech is optimized for syntax-free semantic inference

The way infants learn language is a highly complex adaptive behavior. This behavior chiefly relies on the ability to extract information from the speech they hear and combine it with information from the external environment. Most theories assume that this ability critically hinges on the recognition of at least some syntactic structure. Here, we show that child-directed speech allows for semantic inference without relying on explicit structural information. We simulate the process of semantic inference with machine learning applied to large text collections of two different types of speech, child-directed speech versus adult-directed speech. Taking the core meaning of causality as a test case, we find that in child-directed speech causal meaning can be successfully inferred from simple co-occurrences of neighboring words. By contrast, semantic inference in adult-directed speech fundamentally requires additional access to syntactic structure. These results suggest that child-directed speech is ideally shaped for a learner who has not yet mastered syntactic structure.

Statistical learning occurs during practice while high-order rule learning during rest period

Knowing when the brain learns is crucial for both the comprehension of memory formation and consolidation and for developing new training and neurorehabilitation strategies in healthy and patient populations. Recently, a rapid form of offline learning developing during short rest periods has been shown to account for most of procedural learning, leading to the hypothesis that the brain mainly learns during rest between practice periods. Nonetheless, procedural learning has several subcomponents not disentangled in previous studies investigating learning dynamics, such as acquiring the statistical regularities of the task, or else the high-order rules that regulate its organization. Here we analyzed 506 behavioral sessions of implicit visuomotor deterministic and probabilistic sequence learning tasks, allowing the distinction between general skill learning, statistical learning, and high-order rule learning. Our results show that the temporal dynamics of apparently simultaneous learning processes differ. While high-order rule learning is acquired offline, statistical learning is evidenced online. These findings open new avenues on the short-scale temporal dynamics of learning and memory consolidation and reveal a fundamental distinction between statistical and high-order rule learning, the former benefiting from online evidence accumulation and the latter requiring short rest periods for rapid consolidation.

Learning non-adjacent rules and non-adjacent dependencies from human actions in 9-month-old infants

Seven month old infants can learn simple repetition patterns, such as we-fo-we, and generalize the rules to sequences of new syllables, such as ga-ti-ga. However, repetition rule learning in visual sequences seems more challenging, leading some researchers to claim that this type of rule learning applies preferentially to communicative stimuli. Here we demonstrate that 9-month-old infants can learn repetition rules in sequences of non-communicative dynamic human actions. We also show that when primed with these non-adjacent repetition patterns, infants can learn non-adjacent dependencies that involve memorizing the dependencies between specific human actions-patterns that prior research has shown to be difficult for infants in the visual domain and in speech. We discuss several possible mechanisms that account for the apparent advantage stimuli involving human action sequences has over other kinds of stimuli in supporting non-adjacent dependency learning. We also discuss possible implications for theories of language acquisition.

The Emergence of Richly Organized Semantic Knowledge from Simple Statistics: A Synthetic Review

As adults, we draw upon our ample knowledge about the world to support such vital cognitive feats as using language, reasoning, retrieving knowledge relevant to our current goals, planning for the future, adapting to unexpected events, and navigating through the environment. Our knowledge readily supports these feats because it is not merely a collection of stored facts, but rather functions as an organized, semantic network of concepts connected by meaningful relations. How do the relations that fundamentally organize semantic concepts emerge with development? Here, we cast a spotlight on a potentially powerful but often overlooked driver of semantic organization: Rich statistical regularities that are ubiquitous in both language and visual input. In this synthetic review, we show that a driving role for statistical regularities is convergently supported by evidence from diverse fields, including computational modeling, statistical learning, and semantic development. Finally, we identify a number of key avenues of future research into how statistical regularities may drive the development of semantic organization.

Evidence of ordinal position encoding of sequences extracted from continuous speech

Infants' capacity to extract statistical regularities from sequential information is impressive and well documented. However, statistical learning's underlying mechanism remains mostly unknown, and its role in language acquisition is still under debate. To shed light on these issues, here we address the question of which information human subjects extract and encode after familiarisation with a continuous sequence of stimuli and its dependence on the type of segmentation cues and on the stimuli modality. Specifically, we investigate whether adults and 5-month-old infants learn the syllables' co-occurrence in the stream or generate a representation of the Words that include syllables' ordinal position. We test if subtle pauses signalling word boundaries change the encoding and, in adults, if it varies across modalities. In six behavioural experiments, we show that: (i) Adults and infants learn the streams' statistical structure. (ii) Ordinal encoding emerges in the auditory modality, and pauses enhanced it. However, (iii) ordinal encoding seems to depend on the learning stage and not on pauses marking Words' edges. Interestingly, (iv) for visual presentation of orthographic syllables, we do not find evidence of ordinal encoding in adults. Our results support the emergence, in the auditory modality, of a Word representation where its constituents are associated with an ordinal position at play already early in life, bringing new insights into speech processing and language acquisition. Additionally, we successfully use for the first time pupillometry in an infant segmentation task.

Dysfunctions in Infants' Statistical Learning are Related to Parental Autistic Traits

Statistical learning refers to the ability to extract the statistical relations embedded in a sequence, and it plays a crucial role in the development of communicative and social skills that are impacted in the Autism Spectrum Disorder (ASD). Here, we investigated the relationship between infants’ SL ability and autistic traits in their parents. Using a visual habituation task, we tested infant offspring of adults (non-diagnosed) who show high (HAT infants) versus low (LAT infants) autistic traits. Results demonstrated that LAT infants learned the statistical structure embedded in a visual sequence, while HAT infants failed. Moreover, infants’ SL ability was related to autistic traits in their parents, further suggesting that early dysfunctions in SL might contribute to variabilities in ASD symptoms.

Maternal stress predicts neural responses during auditory statistical learning in 26-month-old children: An event-related potential study

Exposure to high levels of early life stress have been associated with long-term difficulties in learning, behavior, and health, with particular impact evident in the language domain. While some have proposed that the increased stress of living in a low-income household mediates observed associations between socioeconomic status (SES) and child outcomes, considerable individual differences have been observed. The extent to which specific variables associated with socioeconomic status - in particular exposure to stressful life events - influence the neurocognitive mechanisms underlying language acquisition are not well understood. Auditory statistical learning, or the ability to segment a continuous auditory stream based on its statistical properties, develops during early infancy and is one mechanism thought to underlie language learning. The present study used an event-related potential (ERP) paradigm to test whether maternal stress, adjusting for socioeconomic variables (e.g., family income, maternal education) was associated with neurocognitive processes underlying statistical learning in a sample of 26-month-old children (n = 23) from predominantly low- to middle-income backgrounds. Event-related potentials were recorded while children listened to a continuous stream of tri-tone "words" in which tone elements varied in transitional probability. "Tone-words" were presented in random order, such that Tone 1 always predicted Tones 2 and 3 (transitional probability for Tone 3 = 1.0), but Tone 1 appeared randomly. A larger P2 amplitude was observed in response to Tone 3 compared to Tone 1, demonstrating that children implicitly tracked differences in transitional probabilities during passive listening. Maternal reports of stress at 26 months, adjusting for SES, were negatively associated with difference in P2 amplitude between Tones 1 and 3. These findings suggest that maternal stress, within a low-SES context, is associated with the manner in which children process statistical properties of auditory input.

Infants' learning of non-adjacent regularities from visual sequences

Tracking adjacent (AD) and non-adjacent (NAD) dependencies in a sequence of elements is critical for the development of many complex abilities, such as language acquisition and social interaction. While learning of AD in infancy is a domain-general ability that is functioning across different domains, infants' processing of NAD has been reported only for speech sequences. Here, we tested 9- to 12- and 13- to 15-month-olds' ability to extract AxB grammars in visual sequences of unfamiliar elements. Infants were habituated to a series of 3-visual arrays following an AxB grammar in which the first element (A) predicted the third element (B), while intervening X elements changed continuously. Following habituation, infants were tested with 3-item arrays in which initial and final positions were switched (novel) or kept consistent with the habituation phase (familiar). Older infants successfully recognized the familiar AxB grammar at test, whereas the younger group showed some sensitivity to extract to NAD, albeit in a less robust form. This finding provides the first evidence that the ability to track NAD is a domain-general ability that is present also in the visual domain and that the sensitivity to such dependencies is related to developmental changes, as demonstrated in the auditory domain.